Overview

Dataset statistics

Number of variables14
Number of observations390
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.8 KiB
Average record size in memory112.3 B

Variable types

NUM12
CAT2

Warnings

Age is highly correlated with Patient numberHigh correlation
Patient number is highly correlated with AgeHigh correlation
Patient number has unique values Unique

Reproduction

Analysis started2021-08-22 04:39:29.145351
Analysis finished2021-08-22 04:39:54.286342
Duration25.14 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Patient number
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.5
Minimum1
Maximum390
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:54.439344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.45
Q198.25
median195.5
Q3292.75
95-th percentile370.55
Maximum390
Range389
Interquartile range (IQR)194.5

Descriptive statistics

Standard deviation112.7275477
Coefficient of variation (CV)0.5766114969
Kurtosis-1.2
Mean195.5
Median Absolute Deviation (MAD)97.5
Skewness0
Sum76245
Variance12707.5
MonotocityStrictly increasing
2021-08-21T23:39:54.627347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
110.3%
 
24510.3%
 
26710.3%
 
26610.3%
 
26510.3%
 
26410.3%
 
26310.3%
 
26210.3%
 
26110.3%
 
26010.3%
 
Other values (380)38097.4%
 
ValueCountFrequency (%) 
110.3%
 
210.3%
 
310.3%
 
410.3%
 
510.3%
 
ValueCountFrequency (%) 
39010.3%
 
38910.3%
 
38810.3%
 
38710.3%
 
38610.3%
 

Cholesterol
Real number (ℝ≥0)

Distinct153
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.2307692
Minimum78
Maximum443
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:54.814354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile144.45
Q1179
median203
Q3229
95-th percentile290.65
Maximum443
Range365
Interquartile range (IQR)50

Descriptive statistics

Standard deviation44.66600505
Coefficient of variation (CV)0.2155375151
Kurtosis2.679798277
Mean207.2307692
Median Absolute Deviation (MAD)25
Skewness0.9617766361
Sum80820
Variance1995.052007
MonotocityNot monotonic
2021-08-21T23:39:55.017349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
179112.8%
 
20492.3%
 
21571.8%
 
21971.8%
 
19471.8%
 
20361.5%
 
19961.5%
 
17451.3%
 
19351.3%
 
16051.3%
 
Other values (143)32282.6%
 
ValueCountFrequency (%) 
7810.3%
 
11510.3%
 
11810.3%
 
12210.3%
 
12810.3%
 
ValueCountFrequency (%) 
44310.3%
 
40410.3%
 
34710.3%
 
34210.3%
 
33710.3%
 

Glucose
Real number (ℝ≥0)

Distinct116
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.3384615
Minimum48
Maximum385
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:55.227342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile68
Q181
median90
Q3107.75
95-th percentile234.1
Maximum385
Range337
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation53.79818813
Coefficient of variation (CV)0.5012014087
Kurtosis7.905912867
Mean107.3384615
Median Absolute Deviation (MAD)12
Skewness2.711121324
Sum41862
Variance2894.245046
MonotocityNot monotonic
2021-08-21T23:39:55.426350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
85184.6%
 
81153.8%
 
92143.6%
 
87123.1%
 
84123.1%
 
83112.8%
 
77112.8%
 
91102.6%
 
82102.6%
 
76102.6%
 
Other values (106)26768.5%
 
ValueCountFrequency (%) 
4810.3%
 
5210.3%
 
5410.3%
 
5620.5%
 
5710.3%
 
ValueCountFrequency (%) 
38510.3%
 
37110.3%
 
36910.3%
 
34210.3%
 
34110.3%
 

HDL Chol
Real number (ℝ≥0)

Distinct75
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.26666667
Minimum12
Maximum120
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:55.640352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile29.45
Q138
median46
Q359
95-th percentile85.55
Maximum120
Range108
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.27906937
Coefficient of variation (CV)0.3437480643
Kurtosis2.123071215
Mean50.26666667
Median Absolute Deviation (MAD)10
Skewness1.228957201
Sum19604
Variance298.5662382
MonotocityNot monotonic
2021-08-21T23:39:55.836354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
46215.4%
 
44205.1%
 
36205.1%
 
34184.6%
 
42153.8%
 
40143.6%
 
54112.8%
 
37112.8%
 
5892.3%
 
4892.3%
 
Other values (65)24262.1%
 
ValueCountFrequency (%) 
1210.3%
 
1410.3%
 
2310.3%
 
2451.3%
 
2510.3%
 
ValueCountFrequency (%) 
12010.3%
 
11810.3%
 
11710.3%
 
11410.3%
 
11010.3%
 

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.77435897
Minimum19
Maximum92
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:56.014349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q134
median44.5
Q360
95-th percentile76
Maximum92
Range73
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.43591149
Coefficient of variation (CV)0.3513872099
Kurtosis-0.6631610993
Mean46.77435897
Median Absolute Deviation (MAD)13.5
Skewness0.3329066708
Sum18242
Variance270.1391866
MonotocityIncreasing
2021-08-21T23:39:56.493344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40164.1%
 
36133.3%
 
43123.1%
 
41123.1%
 
37112.8%
 
38112.8%
 
63112.8%
 
20102.6%
 
50102.6%
 
60102.6%
 
Other values (58)27470.3%
 
ValueCountFrequency (%) 
1920.5%
 
20102.6%
 
2161.5%
 
2251.3%
 
2371.8%
 
ValueCountFrequency (%) 
9210.3%
 
9110.3%
 
8910.3%
 
8410.3%
 
8310.3%
 

Gender
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
female
228 
male
162 
ValueCountFrequency (%) 
female22858.5%
 
male16241.5%
 
2021-08-21T23:39:56.684348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-08-21T23:39:56.788346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:56.894343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.169230769
Min length4

Height
Real number (ℝ≥0)

Distinct22
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.95128205
Minimum52
Maximum76
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:57.013345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60
Q163
median66
Q369
95-th percentile72
Maximum76
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.918866881
Coefficient of variation (CV)0.05942063231
Kurtosis-0.200402077
Mean65.95128205
Median Absolute Deviation (MAD)3
Skewness0.02997564612
Sum25721
Variance15.35751763
MonotocityNot monotonic
2021-08-21T23:39:57.144346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
634010.3%
 
69369.2%
 
67369.2%
 
65348.7%
 
64338.5%
 
62338.5%
 
66328.2%
 
68266.7%
 
70225.6%
 
71215.4%
 
Other values (12)7719.7%
 
ValueCountFrequency (%) 
5210.3%
 
5510.3%
 
5610.3%
 
5830.8%
 
5992.3%
 
ValueCountFrequency (%) 
7620.5%
 
7530.8%
 
7451.3%
 
7382.1%
 
72143.6%
 

Weight
Real number (ℝ≥0)

Distinct139
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.4076923
Minimum99
Maximum325
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:57.296348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile119
Q1150.25
median173
Q3200
95-th percentile255.55
Maximum325
Range226
Interquartile range (IQR)49.75

Descriptive statistics

Standard deviation40.40782385
Coefficient of variation (CV)0.2277681612
Kurtosis0.7640335329
Mean177.4076923
Median Absolute Deviation (MAD)25
Skewness0.7404730913
Sum69189
Variance1632.792229
MonotocityNot monotonic
2021-08-21T23:39:57.496343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
170133.3%
 
145112.8%
 
183102.6%
 
179102.6%
 
18092.3%
 
16592.3%
 
16082.1%
 
20082.1%
 
16771.8%
 
18561.5%
 
Other values (129)29976.7%
 
ValueCountFrequency (%) 
9910.3%
 
10010.3%
 
10210.3%
 
10520.5%
 
10910.3%
 
ValueCountFrequency (%) 
32510.3%
 
32010.3%
 
30810.3%
 
29010.3%
 
28910.3%
 

BMI
Real number (ℝ≥0)

Distinct193
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.77564103
Minimum15.2
Maximum55.8
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:57.664346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum15.2
5-th percentile19.5
Q124.1
median27.8
Q332.275
95-th percentile41.41
Maximum55.8
Range40.6
Interquartile range (IQR)8.175

Descriptive statistics

Standard deviation6.600914985
Coefficient of variation (CV)0.2293924566
Kurtosis0.8920563266
Mean28.77564103
Median Absolute Deviation (MAD)4
Skewness0.8207569129
Sum11222.5
Variance43.57207864
MonotocityNot monotonic
2021-08-21T23:39:57.821346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30.192.3%
 
27.871.8%
 
27.561.5%
 
25.851.3%
 
27.451.3%
 
26.951.3%
 
25.151.3%
 
29.951.3%
 
24.551.3%
 
2641.0%
 
Other values (183)33485.6%
 
ValueCountFrequency (%) 
15.210.3%
 
1610.3%
 
17.220.5%
 
17.710.3%
 
17.810.3%
 
ValueCountFrequency (%) 
55.810.3%
 
51.410.3%
 
50.510.3%
 
48.610.3%
 
48.410.3%
 

Systolic BP
Real number (ℝ≥0)

Distinct71
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.1333333
Minimum90
Maximum250
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:57.999347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile106.9
Q1122
median136
Q3148
95-th percentile179.55
Maximum250
Range160
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.85952782
Coefficient of variation (CV)0.1666956331
Kurtosis2.392026226
Mean137.1333333
Median Absolute Deviation (MAD)14
Skewness1.098757147
Sum53482
Variance522.558012
MonotocityNot monotonic
2021-08-21T23:39:58.159346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
140369.2%
 
130307.7%
 
110266.7%
 
138194.9%
 
120184.6%
 
150174.4%
 
142164.1%
 
136133.3%
 
122133.3%
 
118123.1%
 
Other values (61)19048.7%
 
ValueCountFrequency (%) 
9010.3%
 
9810.3%
 
10071.8%
 
10220.5%
 
10310.3%
 
ValueCountFrequency (%) 
25010.3%
 
23010.3%
 
22010.3%
 
21810.3%
 
21210.3%
 

Diastolic BP
Real number (ℝ≥0)

Distinct56
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.28974359
Minimum48
Maximum124
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:58.354350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q175
median82
Q390
95-th percentile109.1
Maximum124
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.49819173
Coefficient of variation (CV)0.1620630722
Kurtosis0.1141557845
Mean83.28974359
Median Absolute Deviation (MAD)8
Skewness0.245827774
Sum32483
Variance182.2011799
MonotocityNot monotonic
2021-08-21T23:39:58.530345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
90379.5%
 
80297.4%
 
78225.6%
 
82225.6%
 
70174.4%
 
88164.1%
 
86164.1%
 
100164.1%
 
75143.6%
 
76143.6%
 
Other values (46)18747.9%
 
ValueCountFrequency (%) 
4810.3%
 
5020.5%
 
5210.3%
 
5310.3%
 
5610.3%
 
ValueCountFrequency (%) 
12410.3%
 
12210.3%
 
12020.5%
 
11820.5%
 
11520.5%
 

waist
Real number (ℝ≥0)

Distinct30
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.86923077
Minimum26
Maximum56
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:58.677343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29
Q133
median37
Q341
95-th percentile48
Maximum56
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.760947055
Coefficient of variation (CV)0.1521273852
Kurtosis-0.1536526614
Mean37.86923077
Median Absolute Deviation (MAD)4
Skewness0.4753161478
Sum14769
Variance33.18851097
MonotocityNot monotonic
2021-08-21T23:39:58.806346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
37317.9%
 
40276.9%
 
38276.9%
 
33276.9%
 
36256.4%
 
39235.9%
 
34235.9%
 
32225.6%
 
31215.4%
 
35194.9%
 
Other values (20)14537.2%
 
ValueCountFrequency (%) 
2620.5%
 
2710.3%
 
2871.8%
 
29112.8%
 
30102.6%
 
ValueCountFrequency (%) 
5610.3%
 
5510.3%
 
5320.5%
 
5220.5%
 
5141.0%
 

hip
Real number (ℝ≥0)

Distinct32
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.99230769
Minimum30
Maximum64
Zeros0
Zeros (%)0.0%
Memory size3.0 KiB
2021-08-21T23:39:58.944343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35
Q139
median42
Q346
95-th percentile54
Maximum64
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.66434229
Coefficient of variation (CV)0.1317524598
Kurtosis0.8625715722
Mean42.99230769
Median Absolute Deviation (MAD)3
Skewness0.7943390011
Sum16767
Variance32.08477358
MonotocityNot monotonic
2021-08-21T23:39:59.091347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
41359.0%
 
39359.0%
 
40338.5%
 
43287.2%
 
38287.2%
 
42276.9%
 
47225.6%
 
44215.4%
 
46205.1%
 
45205.1%
 
Other values (22)12131.0%
 
ValueCountFrequency (%) 
3010.3%
 
3210.3%
 
3382.1%
 
3461.5%
 
35112.8%
 
ValueCountFrequency (%) 
6410.3%
 
6220.5%
 
6010.3%
 
5910.3%
 
5851.3%
 

Diabetes
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
No diabetes
330 
Diabetes
60 
ValueCountFrequency (%) 
No diabetes33084.6%
 
Diabetes6015.4%
 
2021-08-21T23:39:59.246343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-08-21T23:39:59.325343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:59.415343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.53846154
Min length8

Interactions

2021-08-21T23:39:32.525352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:32.701355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:32.852344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.025353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.188354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.359346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.503346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.654354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:33.804344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.049351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.238345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.394349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.540348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.695349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:34.843342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.001348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.143347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.283346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.424348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.568348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.726350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:35.874343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.035343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.171343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.307346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.476348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.644349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.774343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:36.914347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.048347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.183349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.311346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.449343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.586344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.742343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:37.888347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.161347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.298346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.432343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.567349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.698347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:38.861343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.004347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.127343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.260347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.391351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.524347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.643346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.771346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:39.910347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.050346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.184343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.319347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.452347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.581347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.706342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.843346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:40.980352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.143342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.297342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.425346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.546346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.670346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.802343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:41.921345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.042346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.150343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.265343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.398343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.526346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.644346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:42.914343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.035342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.165343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.294343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.444353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.586352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.710343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.822343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:43.935345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.060346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.188343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.315347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.428346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.543347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.689352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.829345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:44.969347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.101346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.231346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.364344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.502359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.649353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.818342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:45.995353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.137343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.285346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.430343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.579343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.723345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.856346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:46.986346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.119344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.249346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.393342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.525346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.670347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.801343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:47.937347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:48.127352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:48.291342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:48.433346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:48.561346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:48.884348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.044345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.215348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.414342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.590342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.758349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:49.918342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.069343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.216343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.376353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.547342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.690346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.818346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:50.945349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.077346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.211343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.355350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.521343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.671343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.827343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:51.979347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.122346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.266350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.404347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.541345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.719351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:52.903343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:53.053346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:53.188348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:53.323346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:53.458342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-21T23:39:59.546345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-21T23:39:59.833344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-21T23:40:00.075348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-21T23:40:00.322342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-21T23:40:00.530346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-21T23:39:53.757344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-21T23:39:54.123343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

Patient numberCholesterolGlucoseHDL CholAgeGenderHeightWeightBMISystolic BPDiastolic BPwaisthipDiabetes
01193774919female6111922.5118703238No diabetes
12146794119female6013526.4108583340No diabetes
23217755420female6718729.3110724045No diabetes
34226977020female6411419.6122643139No diabetes
45164916720female7014120.2122863239No diabetes
56170696420female6416127.6108703740No diabetes
67149774920female6211521.0105823137No diabetes
78164716320male7214519.7108782936No diabetes
892301126420male6715924.9100903139No diabetes
9101791056020female5817035.51401003446No diabetes

Last rows

Patient numberCholesterolGlucoseHDL CholAgeGenderHeightWeightBMISystolic BPDiastolic BPwaisthipDiabetes
380381157924780male7121229.6156884748No diabetes
3813822521618780female6216229.61601004441Diabetes
3823832711214081female6415827.1146763643No diabetes
383384240884982female6317030.1180864146No diabetes
3843852551123482male6616326.3179893743No diabetes
3853862271054483female5912525.2150903540No diabetes
3863872262795284female6019237.5144884148Diabetes
3873883019011889female6111521.7218903141No diabetes
38838923218411491female6112724.0170823538Diabetes
389390165946992female6221739.7160825151No diabetes